Treffer: Bayesian inference with node aggregation for information retrieval
Title:
Bayesian inference with node aggregation for information retrieval
Authors:
Source:
TREC-2: Text retrieval conferenceNIST special publication. (500215):151-161
Publisher Information:
Gaithersburg, MD: National Institute of Standards and Technology, 1994.
Publication Year:
1994
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Science technology, industry, Sciences et technologies, industries, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Sciences de l'information. Documentation, Information science. Documentation, Systèmes de recherche d'informations. Système de gestion documentaire et d'information, Information retrieval systems. Information and document management system, Systèmes de recherche d'information, Information retrieval systems, Sciences de l'information et de la communication, Information and communication sciences, Système de recherche documentaire. Système de gestion documentaire et d'information, Informatique documentaire, Documentation data processing, Información documental, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Relation homme machine, Man machine relation, Relación hombre máquina, Assistance utilisateur, User assistance, Asistencia usuario, Dispositif expérimental, Experimental device, Dispositivo experimental, Essai, Test, Ensayo, Evaluation performance, Performance evaluation, Evaluación prestación, Implémentation, Implementation, Ejecución, Inférence, Inference, Inferencia, Mode conversationnel, Interactive mode, Modo conversacional, Modèle probabiliste, Probabilistic model, Modelo probabilista, Probabilité conditionnelle, Conditional probability, Probabilidad condicional, Recherche documentaire, Document retrieval, Recuperación documental, Recherche développement, Research and development, Investigación desarrollo, Règle inférence, Inference rule, Regla inferencia, Système documentaire, Document retrieval system, Sistema recuperación documental, Système recherche, Search system, Sistema investigación, Traitement information, Information processing, Procesamiento información, Orienté question, Question oriented, Réseau Bayesien, Bayesian network, TREC-2, Thème intérêt, Interest topic
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Inst. decision systems res., Palo Alto CA 94306, United States
ISSN:
1048-776X
Rights:
Copyright 1995 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Sciences of information and communication. Documentation
FRANCIS
FRANCIS
Accession Number:
edscal.3464814
Database:
PASCAL Archive
Weitere Informationen
In this paper we describe research that adapts and applies Bayesian networks, a new technology for probabilistic representation and inference, to information retrieval. Our research is directed at developing a probabilistic information retrieval architecture that is oriented towards assisting users that have stable information needs in routing large amounts of time-sensitive material; gives users an intuitive language with which to specify their information needs; requires modest computational resources (memory, CPU need); can integrate relevance feedback and training data with users'judgments to incrementally improve retrieval performance.